Model Parameters

Row

Model Parameters

Model Instance

  • Planning horizon is decreased from 24 weeks to 10 weeks
  • Maximum tracked wait is decreased from 6 weeks to 4 weeks
  • There are 3 surgeries instead of 6 surgeries
  • Number of priorities is set to 1

Simulation Parameters

  • 30 Replications
  • 1000 weeks duration
  • 250 weeks warm up

Surgeries

  • Surgery 1 - 1. SPINE POSTERIOR DECOMPRESSION/LAMINECTOMY LUMBAR
  • Surgery 4 - 4. SPINE POST CERV DECOMPRESSION AND FUSION W INSTR
  • Surgery 6 - 6. SPINE POSTERIOR DISCECTOMY LUMBAR

Arrival Rate

It was set to be 95% of the capacity, however due to transitions, the resource usage should be higher than 95%
Surgery Complexity Arrival_Adjusted Arrival_Original Rationale
Surgery 1 Complexity 1 1.23 1.0000 once per week
Surgery 1 Complexity 2 0.62 0.5000 once per two weeks
Surgery 4 Complexity 1 0.14 0.0833 once per 3 months
Surgery 4 Complexity 2 0.10 0.0625 once per 4 months
Surgery 6 Complexity 1 1.23 1.0000 once per week
Surgery 6 Complexity 2 0.62 0.5000 once per 2 weeks

Row

Resource Usage

Surgery Complexity Resource_Type Usage
Surgery 1 Complexity 1 Admissions 0.0
Surgery 1 Complexity 1 OR_Time 3.0
Surgery 1 Complexity 2 Admissions 1.0
Surgery 1 Complexity 2 OR_Time 4.0
Surgery 4 Complexity 1 Admissions 1.0
Surgery 4 Complexity 1 OR_Time 4.0
Surgery 4 Complexity 2 Admissions 1.0
Surgery 4 Complexity 2 OR_Time 5.5
Surgery 6 Complexity 1 Admissions 0.0
Surgery 6 Complexity 1 OR_Time 1.5
Surgery 6 Complexity 2 Admissions 0.0
Surgery 6 Complexity 2 OR_Time 2.5

Resource Capacity

Resouce Capacity_Weekly Unit
Admissions 1.50 Patients Admitted per week
OR_Time 11.25 OR Hours per week

NP, 1

Row

Policy Description

The policy description is based on a graph to the right

  • The left column shows MDP policy, the right column shows Myopic Policy
  • The top row shows scheduling costs, adjusted for resource usage, the bottom is not adjusted
  • The bottom row shows in which days the policy will allow scheduling.
  • The top row shows approximate order of patient scheduling.

Additionally the far right graph shows the approximate decision making.

Policy:

  • MDP will schedule into the first 2 days only.

  • In this approximate order:

    • Surgery 6, Complexity 1
    • Surgery 1, Complexity 1
    • Surgery 6, Complexity 2
    • Surgery 4, Complexity 2
    • Surgery 4, Complexity 1 / Surgery 1, Complexity 2

Policy Math Graph

Policy Evidence Graph

Row

Wait Times in weeks

policy Overall Surgery1 Surgery4 Surgery6
MDP 11.83 += 14.91 19.71 += 19.68 36.56 += 19.35 0.59 += 0.22
myopic 14.15 += 12.53 24.2 += 17.01 26.3 += 8.32 2.36 += 0.86

Wait List Size

policy Overall Surgery1 Surgery4 Surgery6
MDP 47.68 += 9.53 37.7 += 8.96 8.9 += 2.42 1.09 += 0.45
myopic 56.53 += 11.18 45.76 += 10.22 6.43 += 1.46 4.35 += 0.85

Transitions per week

policy Overall Surgery1 Surgery4 Surgery6
MDP 1.96 += 0.1 1.04 += 0.11 23.25 += 1.11 0.1 += 0.03
myopic 10.06 += 0.43 12.58 += 0.6 42.65 += 2.2 3.2 += 0.2

Utilization

policy bed OR
MDP 53.51 += 12.85 93.48 += 7.07
myopic 64.89 += 11.87 95.48 += 6.82

Row

Reschedules

policy Overall Surgery1 Surgery4 Surgery6
MDP 3.09 += 0.09 4.4 += 0.19 5.22 += 0.6 1.47 += 0.1
myopic 12.82 += 0.33 15.3 += 0.38 62.53 += 2.78 3.8 += 0.22

Wait List Size by Group

Reschedules by Group

NP, 1.1

Row

Policy Description

The policy description is based on a graph to the right

  • The left column shows MDP policy, the right column shows Myopic Policy
  • The top row shows scheduling costs, adjusted for resource usage, the bottom is not adjusted
  • The bottom row shows in which days the policy will allow scheduling.
  • The top row shows approximate order of patient scheduling.

Additionally the far right graph shows the approximate decision making

Policy:

  • MDP will schedule into the first 2 days only.

  • In this approximate order:

    • Surgery 6, Complexity 1
    • Surgery 6, Complexity 2
    • Surgery 1, Complexity 1
    • Surgery 4, Complexity 2
    • Surgery 4, Complexity 1 / Surgery 1, Complexity 2

Policy Math Graph

Policy Evidence Graph

Row

Wait Times in weeks

policy Overall Surgery1 Surgery4 Surgery6
MDP 1.41 += 1.55 2.29 += 2.14 2.61 += 1.26 0.37 += 0.19
myopic 1.64 += 0.87 2.23 += 0.96 4.19 += 1.51 0.72 += 0.38

Wait List Size

policy Overall Surgery1 Surgery4 Surgery6
MDP 5.58 += 1.97 4.27 += 1.71 0.63 += 0.34 0.68 += 0.36
myopic 6.5 += 2.11 4.16 += 1.39 1.01 += 0.45 1.32 += 0.63

Transitions per week

policy Overall Surgery1 Surgery4 Surgery6
MDP 0.5 += 0.06 0.28 += 0.06 5.65 += 0.76 0.04 += 0.02
myopic 2.22 += 0.22 2.36 += 0.26 13.3 += 1.4 0.62 += 0.12

Utilization

policy bed OR
MDP 57.33 += 13.38 86.51 += 8.1
myopic 58.69 += 10.8 86.85 += 7.59

Row

Reschedules

policy Overall Surgery1 Surgery4 Surgery6
MDP 1.25 += 0.07 1.66 += 0.13 3.09 += 0.44 0.58 += 0.08
myopic 3.93 += 0.26 5.48 += 0.36 11.91 += 1.36 1.31 += 0.14

Wait List Size by Group

Reschedules by Group

NP, 1.2

Row

Policy Description

The policy description is based on a graph to the right

  • The left column shows MDP policy, the right column shows Myopic Policy
  • The top row shows scheduling costs, adjusted for resource usage, the bottom is not adjusted
  • The bottom row shows in which days the policy will allow scheduling.
  • The top row shows approximate order of patient scheduling.

Additionally the far right graph shows the approximate decision making

Policy:

  • MDP will schedule into the first 2 days only.

  • In this approximate order:

    • Surgery 6, Complexity 1
    • Surgery 6, Complexity 2
    • Surgery 1, Complexity 1
    • Surgery 4, Complexity 2
    • Surgery 4, Complexity 1 / Surgery 1, Complexity 2

Policy Math Graph

Policy Evidence Graph

Row

Wait Times in weeks

policy Overall Surgery1 Surgery4 Surgery6
MDP 0.58 += 0.56 0.82 += 0.7 1.51 += 0.9 0.22 += 0.15
myopic 0.82 += 0.48 1.15 += 0.54 2.27 += 0.76 0.29 += 0.21

Wait List Size

policy Overall Surgery1 Surgery4 Surgery6
MDP 2.3 += 1 1.53 += 0.76 0.36 += 0.25 0.4 += 0.28
myopic 3.22 += 1.19 2.14 += 0.82 0.55 += 0.3 0.53 += 0.38

Transitions per week

policy Overall Surgery1 Surgery4 Surgery6
MDP 0.2 += 0.03 0.09 += 0.02 2.45 += 0.4 0.01 += 0.01
myopic 0.79 += 0.09 0.54 += 0.11 7.88 += 0.87 0.11 += 0.04

Utilization

policy bed OR
MDP 57.32 += 14.26 79.23 += 8.89
myopic 57.47 += 11.04 79.31 += 8.21

Row

Reschedules

policy Overall Surgery1 Surgery4 Surgery6
MDP 0.51 += 0.06 0.7 += 0.09 1.39 += 0.36 0.19 += 0.04
myopic 1.51 += 0.14 2.38 += 0.22 3.46 += 0.57 0.36 += 0.07

Wait List Size by Group

Reschedules by Group

NP, 1.3

Row

Policy Description

The policy description is based on a graph to the right

  • The left column shows MDP policy, the right column shows Myopic Policy
  • The top row shows scheduling costs, adjusted for resource usage, the bottom is not adjusted
  • The bottom row shows in which days the policy will allow scheduling.
  • The top row shows approximate order of patient scheduling.

Additionally the far right graph shows the approximate decision making

Policy:

  • MDP will schedule into the first 2 days only.

  • In this approximate order:

    • Surgery 6, Complexity 1
    • Surgery 6, Complexity 2
    • Surgery 1, Complexity 1
    • Surgery 4, Complexity 2
    • Surgery 4, Complexity 1 / Surgery 1, Complexity 2

Policy Math Graph

Policy Evidence Graph

Row

Wait Times in weeks

policy Overall Surgery1 Surgery4 Surgery6
MDP 0.36 += 0.37 0.51 += 0.48 0.88 += 0.46 0.13 += 0.12
myopic 0.56 += 0.39 0.81 += 0.45 1.77 += 0.62 0.14 += 0.13

Wait List Size

policy Overall Surgery1 Surgery4 Surgery6
MDP 1.41 += 0.72 0.96 += 0.55 0.21 += 0.18 0.25 += 0.21
myopic 2.19 += 0.87 1.51 += 0.64 0.43 += 0.26 0.25 += 0.24

Transitions per week

policy Overall Surgery1 Surgery4 Surgery6
MDP 0.08 += 0.02 0.02 += 0.01 1.15 += 0.31 0 += 0
myopic 0.4 += 0.05 0.15 += 0.04 5.31 += 0.63 0.02 += 0.01

Utilization

policy bed OR
MDP 57.3 += 14.98 73.11 += 9.21
myopic 57.28 += 11.45 73.13 += 8.49

Row

Reschedules

policy Overall Surgery1 Surgery4 Surgery6
MDP 0.16 += 0.03 0.22 += 0.05 0.59 += 0.27 0.05 += 0.02
myopic 0.53 += 0.06 0.85 += 0.11 1.49 += 0.37 0.09 += 0.03

Wait List Size by Group

Reschedules by Group

P, 1

Row

Policy Description

The policy description is based on a graph to the right

  • The left column shows MDP policy, the right column shows Myopic Policy
  • The top row shows scheduling costs, adjusted for resource usage, the bottom is not adjusted
  • The bottom row shows in which days the policy will allow scheduling.
  • The top row shows approximate order of patient scheduling.

Additionally the far right graph shows the approximate decision making

Policy:

  • MDP will schedule S6 and S1C1 into the first 2 days only. S4 and S1C2 will be scheduled into the entire planning horizon.

  • In this approximate order:

    • Surgery 4, Complexity 1 / Surgery 1, Complexity 2
    • Surgery 4, Complexity 2
    • Surgery 6, Complexity 1
    • Surgery 6, Complexity 2
    • Surgery 1, Complexity 1

Policy Math Graph

Policy Evidence Graph

Row

Wait Times in weeks

policy Overall Surgery1 Surgery4 Surgery6
MDP 11.87 += 8.68 16.33 += 5.53 31.65 += 27.69 4.74 += 3.77
myopic 22.21 += 16.42 16.4 += 5.38 75.42 += 51.36 21.12 += 12.27

Wait List Size

policy Overall Surgery1 Surgery4 Surgery6
MDP 47.32 += 9.29 30.63 += 5.47 7.78 += 3.7 8.92 += 3.2
myopic 88.6 += 20.81 30.81 += 6.06 18.66 += 7.93 39.14 += 9.61

Transitions per week

policy Overall Surgery1 Surgery4 Surgery6
MDP 26.33 += 0.98 52.45 += 1.99 0.07 += 0.09 3.27 += 0.28
myopic 31.54 += 2.63 53.06 += 4.56 9.19 += 1.67 12.64 += 0.89

Utilization

policy bed OR
MDP 119.93 += 12.07 102.53 += 8.47
myopic 117.43 += 13.99 100.2 += 6.89

Row

Reschedules

policy Overall Surgery1 Surgery4 Surgery6
MDP 64.54 += 1.64 52.17 += 3 0 += NA 85.44 += 5.78
myopic 78.62 += 4.05 46.86 += 7 0 += NA 121.15 += 14.38

Wait List Size by Group

Reschedules by Group

P, 1.1

Row

Policy Description

The policy description is based on a graph to the right

  • The left column shows MDP policy, the right column shows Myopic Policy
  • The top row shows scheduling costs, adjusted for resource usage, the bottom is not adjusted
  • The bottom row shows in which days the policy will allow scheduling.
  • The top row shows approximate order of patient scheduling.

Additionally the far right graph shows the approximate decision making

Policy:

  • MDP will schedule S6 and S1C1 into the first 2 days only. S4 and S1C2 will be scheduled into the entire planning horizon.

  • In this approximate order:

    • Surgery 4, Complexity 1 / Surgery 1, Complexity 2
    • Surgery 4, Complexity 2
    • Surgery 6, Complexity 1
    • Surgery 1, Complexity 1
    • Surgery 6, Complexity 2

Policy Math Graph

Policy Evidence Graph

Row

Wait Times in weeks

policy Overall Surgery1 Surgery4 Surgery6
MDP 3.45 += 1.95 5.22 += 2.45 0.96 += 1.44 1.98 += 0.98
myopic 1.43 += 0.92 1.77 += 1.1 0.33 += 0.33 1.23 += 0.72

Wait List Size

policy Overall Surgery1 Surgery4 Surgery6
MDP 13.62 += 3.45 9.75 += 2.7 0.23 += 0.26 3.64 += 1.35
myopic 5.66 += 2.36 3.32 += 1.4 0.08 += 0.11 2.27 += 1.1

Transitions per week

policy Overall Surgery1 Surgery4 Surgery6
MDP 10.83 += 0.72 20.86 += 1.44 0.02 += 0.03 2.07 += 0.15
myopic 3.3 += 0.46 5.72 += 0.76 0.23 += 0.14 1.25 += 0.23

Utilization

policy bed OR
MDP 81.2 += 19.25 89.23 += 11.1
myopic 62.65 += 19.18 87.11 += 8.43

Row

Reschedules

policy Overall Surgery1 Surgery4 Surgery6
MDP 35.69 += 1.28 46.42 += 1.61 0 += NA 29.48 += 1.9
myopic 21.91 += 1.47 35.4 += 2.2 0 += NA 11.1 += 1.09

Wait List Size by Group

Reschedules by Group

P, 1.2

Row

Policy Description

The policy description is based on a graph to the right

  • The left column shows MDP policy, the right column shows Myopic Policy
  • The top row shows scheduling costs, adjusted for resource usage, the bottom is not adjusted
  • The bottom row shows in which days the policy will allow scheduling.
  • The top row shows approximate order of patient scheduling.

Additionally the far right graph shows the approximate decision making

Policy:

  • MDP will schedule S6 and S1C1 into the first 2 days only. S4 and S1C2 will be scheduled into the entire planning horizon.

  • In this approximate order:

    • Surgery 4, Complexity 1 / Surgery 1, Complexity 2
    • Surgery 4, Complexity 2
    • Surgery 6, Complexity 1
    • Surgery 1, Complexity 1
    • Surgery 6, Complexity 2

Policy Math Graph

Policy Evidence Graph

Row

Wait Times in weeks

policy Overall Surgery1 Surgery4 Surgery6
MDP 1.69 += 1.11 2.1 += 1.38 0.28 += 0.46 1.45 += 0.79
myopic 0.6 += 0.41 0.76 += 0.5 0.24 += 0.24 0.48 += 0.29

Wait List Size

policy Overall Surgery1 Surgery4 Surgery6
MDP 6.65 += 2.72 3.92 += 1.82 0.07 += 0.12 2.66 += 1.21
myopic 2.37 += 1.19 1.42 += 0.76 0.06 += 0.1 0.88 += 0.54

Transitions per week

policy Overall Surgery1 Surgery4 Surgery6
MDP 4.14 += 0.44 7.43 += 0.81 0 += 0 1.33 += 0.15
myopic 0.85 += 0.12 1.53 += 0.22 0.23 += 0.18 0.25 += 0.07

Utilization

policy bed OR
MDP 65.1 += 19.25 79.95 += 11.29
myopic 58.29 += 18.85 79.28 += 9.17

Row

Reschedules

policy Overall Surgery1 Surgery4 Surgery6
MDP 19.91 += 1.19 11.8 += 0.72 0 += NA 30.78 += 2.09
myopic 9.54 += 0.68 16.25 += 1.14 0 += NA 3.99 += 0.37

Wait List Size by Group

Reschedules by Group

P, 1.3

Row

Policy Description

The policy description is based on a graph to the right

  • The left column shows MDP policy, the right column shows Myopic Policy
  • The top row shows scheduling costs, adjusted for resource usage, the bottom is not adjusted
  • The bottom row shows in which days the policy will allow scheduling.
  • The top row shows approximate order of patient scheduling.

Additionally the far right graph shows the approximate decision making

Policy:

  • MDP will schedule S6 and S1C1 into the first 2 days only. S4 and S1C2 will be scheduled into the entire planning horizon.

  • In this approximate order:

    • Surgery 4, Complexity 1 / Surgery 1, Complexity 2
    • Surgery 4, Complexity 2
    • Surgery 6, Complexity 1
    • Surgery 1, Complexity 1
    • Surgery 6, Complexity 2

Policy Math Graph

Policy Evidence Graph

Row

Wait Times in weeks

policy Overall Surgery1 Surgery4 Surgery6
MDP 0.75 += 0.61 0.91 += 0.76 0.14 += 0.18 0.66 += 0.44
myopic 0.34 += 0.26 0.44 += 0.31 0.23 += 0.23 0.26 += 0.19

Wait List Size

policy Overall Surgery1 Surgery4 Surgery6
MDP 2.94 += 1.63 1.7 += 1.06 0.03 += 0.07 1.21 += 0.77
myopic 1.35 += 0.81 0.83 += 0.53 0.06 += 0.09 0.47 += 0.37

Transitions per week

policy Overall Surgery1 Surgery4 Surgery6
MDP 1.56 += 0.19 2.72 += 0.33 0.02 += 0.03 0.57 += 0.1
myopic 0.35 += 0.07 0.64 += 0.13 0.14 += 0.09 0.08 += 0.03

Utilization

policy bed OR
MDP 59.86 += 18.93 73.33 += 10.67
myopic 57.62 += 18.8 73.1 += 9.47

Row

Reschedules

policy Overall Surgery1 Surgery4 Surgery6
MDP 8.34 += 0.54 11.98 += 0.8 0 += NA 5.74 += 0.51
myopic 4.26 += 0.43 7.63 += 0.77 0 += NA 1.4 += 0.21

Wait List Size by Group

Reschedules by Group

---
title: "Report"
date: "`r Sys.Date()`"
output: 
  flexdashboard::flex_dashboard:
    orientation: rows
    social: menu
    source_code: embed
---

```{r setup, include=FALSE}
## Global options
library(reticulate)
library(knitr)
library(flexdashboard)
library(scales)
library(here)
library(tidyverse)
library(readr)
library(plotly)
library(tidyverse)
library(here)
knitr::opts_chunk$set(cache = TRUE)
source(here('modules','data_funcs.R'))

# PARAMS
warm <- 250
dur <- 1000
repl <- 30
path <- here('data','full-sm')



# NO CUU - OR * 1
modif <- '0-1'
dt_pl_n10 <- generate_summary(path, modif, dur, warm, repl)
dt_sa_n10 <- generate_summary_sa(path, modif, dur, warm, repl)
dt_zs_n10 <- generate_summary_zs(path, modif, FALSE)

# NO CUU - OR * 1.1
modif <- '0-1.1'
dt_pl_n11 <- generate_summary(path, modif, dur, warm, repl)
dt_sa_n11 <- generate_summary_sa(path, modif, dur, warm, repl)
dt_zs_n11 <- generate_summary_zs(path, modif, FALSE)

# NO CUU - OR * 1.2
modif <- '0-1.2'
dt_pl_n12 <- generate_summary(path, modif, dur, warm, repl)
dt_sa_n12 <- generate_summary_sa(path, modif, dur, warm, repl)
dt_zs_n12 <- generate_summary_zs(path, modif, FALSE)

# NO CUU - OR * 1.3
modif <- '0-1.3'
dt_pl_n13 <- generate_summary(path, modif, dur, warm, repl)
dt_sa_n13 <- generate_summary_sa(path, modif, dur, warm, repl)
dt_zs_n13 <- generate_summary_zs(path, modif, FALSE)



# CUU - OR * 1
modif <- '1000-1'
dt_pl_y10 <- generate_summary(path, modif, dur, warm, repl)
dt_sa_y10 <- generate_summary_sa(path, modif, dur, warm, repl)
dt_zs_y10 <- generate_summary_zs(path, modif, TRUE)

# CUU - OR * 1.1
modif <- '1000-1.1'
dt_pl_y11 <- generate_summary(path, modif, dur, warm, repl)
dt_sa_y11 <- generate_summary_sa(path, modif, dur, warm, repl)
dt_zs_y11 <- generate_summary_zs(path, modif, TRUE)

# CUU - OR * 1.2
modif <- '1000-1.2'
dt_pl_y12 <- generate_summary(path, modif, dur, warm, repl)
dt_sa_y12 <- generate_summary_sa(path, modif, dur, warm, repl)
dt_zs_y12 <- generate_summary_zs(path, modif, TRUE)

# CUU - OR * 1.3
modif <- '1000-1.3'
dt_pl_y13 <- generate_summary(path, modif, dur, warm, repl)
dt_sa_y13 <- generate_summary_sa(path, modif, dur, warm, repl)
dt_zs_y13 <- generate_summary_zs(path, modif, TRUE)



# MODEL DATA SUMMARY
arrival_rate <- data.frame(
  Surgery = c('Surgery 1', 'Surgery 1', 'Surgery 4', 
              'Surgery 4', 'Surgery 6', 'Surgery 6'),
  Complexity = c('Complexity 1', 'Complexity 2', 'Complexity 1', 
                 'Complexity 2', 'Complexity 1', 'Complexity 2'),
  "Arrival_Adjusted" = c(1.23, 0.62, 0.14, 0.10, 1.23, 0.62),
  "Arrival_Original" = c(1, 0.5, 0.0833, 0.0625, 1, 0.5), 
  Rationale = c("once per week", "once per two weeks", "once per 3 months", 
                "once per 4 months", "once per week", "once per 2 weeks")
)

resource_usage <- data.frame(
  Surgery = c('Surgery 1', 'Surgery 1', 'Surgery 1', 'Surgery 1', 
              'Surgery 4', 'Surgery 4', 'Surgery 4', 'Surgery 4', 
              'Surgery 6', 'Surgery 6', 'Surgery 6', 'Surgery 6'),
  Complexity = c('Complexity 1', 'Complexity 1', 'Complexity 2', 
                 'Complexity 2', 'Complexity 1', 'Complexity 1', 
                 'Complexity 2', 'Complexity 2', 'Complexity 1', 
                 'Complexity 1', 'Complexity 2', 'Complexity 2'),
  Resource_Type = c('Admissions', 'OR_Time','Admissions', 'OR_Time',
                    'Admissions', 'OR_Time','Admissions', 'OR_Time',
                    'Admissions', 'OR_Time','Admissions', 'OR_Time'), 
  Usage = c(0,3,1,4,1,4,1,5.5,0,1.5,0,2.5)
)

resource_capacity <- data.frame(
  Resouce = c('Admissions', 'OR_Time'),
  Capacity_Weekly = c(1.5, 11.25),
  Unit = c("Patients Admitted per week", "OR Hours per week")
)
```

Model Parameters
=======================================================================

Row
-----------------------------------------------------------------------

### Model Parameters
**Model Instance**

* Planning horizon is decreased from 24 weeks to 10 weeks
* Maximum tracked wait is decreased from 6 weeks to 4 weeks
* There are 3 surgeries instead of 6 surgeries
* Number of priorities is set to 1

**Simulation Parameters**

* 30 Replications
* 1000 weeks duration
* 250 weeks warm up

**Surgeries**

* Surgery 1 - 1. SPINE POSTERIOR DECOMPRESSION/LAMINECTOMY LUMBAR
* Surgery 4 - 4. SPINE POST CERV DECOMPRESSION AND FUSION W INSTR
* Surgery 6 - 6. SPINE POSTERIOR DISCECTOMY LUMBAR

### Arrival Rate
It was set to be 95% of the capacity, however due to transitions, the resource usage should be higher than 95%
``` {r echo=FALSE, cache=FALSE}
kable(arrival_rate)
```

Row
-----------------------------------------------------------------------

### Resource Usage
```{r echo=FALSE, cache=FALSE}
kable(resource_usage)
```

### Resource Capacity
```{r echo=FALSE, cache=FALSE}
kable(resource_capacity)
```





NP, 1
=======================================================================

Row {data-height=650}
-----------------------------------------------------------------------

### Policy Description
The policy description is based on a graph to the right

* The left column shows MDP policy, the right column shows Myopic Policy
* The top row shows scheduling costs, adjusted for resource usage, the bottom is not adjusted
* The bottom row shows in which days the policy will allow scheduling.
* The top row shows approximate order of patient scheduling.

Additionally the far right graph shows the approximate decision making.

Policy:

* MDP will schedule into the first 2 days only. 
* In this approximate order:

  * Surgery 6, Complexity 1
  * Surgery 1, Complexity 1
  * Surgery 6, Complexity 2
  * Surgery 4, Complexity 2
  * Surgery 4, Complexity 1 / Surgery 1, Complexity 2

### Policy Math Graph

```{r echo=FALSE}
dt_zs_n10$zf_plt %>% ggplotly()
```

### Policy Evidence Graph

```{r echo=FALSE}
dt_sa_n10$res_plot$sched_plt %>% ggplotly()
```

Row 
-----------------------------------------------------------------------

### Wait Times in weeks
```{r echo=FALSE, cache=FALSE}
kable(dt_pl_n10$results$pw)
```

### Wait List Size
```{r echo=FALSE, cache=FALSE}
kable(dt_pl_n10$results$wtl)
```

### Transitions per week
```{r echo=FALSE, cache=FALSE}
kable(dt_pl_n10$results$tr)
```

### Utilization
```{r echo=FALSE, cache=FALSE}
kable(dt_pl_n10$results$util)
```

Row
-----------------------------------------------------------------------

### Reschedules
```{r echo=FALSE, cache=FALSE}
kable(dt_pl_n10$results$rsc)
```

### Wait List Size by Group
```{r echo=FALSE, cache=FALSE}
dt_sa_n10$res_plot$waitlist_plt %>% ggplotly()
```

### Reschedules by Group
```{r echo=FALSE, cache=FALSE}
dt_sa_n10$res_plot$rsc_plt %>% ggplotly()
```





NP, 1.1
=======================================================================

Row {data-height=650}
-----------------------------------------------------------------------

### Policy Description
The policy description is based on a graph to the right

* The left column shows MDP policy, the right column shows Myopic Policy
* The top row shows scheduling costs, adjusted for resource usage, the bottom is not adjusted
* The bottom row shows in which days the policy will allow scheduling.
* The top row shows approximate order of patient scheduling.

Additionally the far right graph shows the approximate decision making 

Policy: 

* MDP will schedule into the first 2 days only. 
* In this approximate order:

  * Surgery 6, Complexity 1
  * Surgery 6, Complexity 2
  * Surgery 1, Complexity 1
  * Surgery 4, Complexity 2
  * Surgery 4, Complexity 1 / Surgery 1, Complexity 2


### Policy Math Graph

```{r echo=FALSE}
dt_zs_n11$zf_plt %>% ggplotly()
```

### Policy Evidence Graph

```{r echo=FALSE}
dt_sa_n11$res_plot$sched_plt %>% ggplotly()
```

Row 
-----------------------------------------------------------------------

### Wait Times in weeks
```{r echo=FALSE, cache=FALSE}
kable(dt_pl_n11$results$pw)
```

### Wait List Size
```{r echo=FALSE, cache=FALSE}
kable(dt_pl_n11$results$wtl)
```

### Transitions per week
```{r echo=FALSE, cache=FALSE}
kable(dt_pl_n11$results$tr)
```

### Utilization
```{r echo=FALSE, cache=FALSE}
kable(dt_pl_n11$results$util)
```

Row
-----------------------------------------------------------------------

### Reschedules
```{r echo=FALSE, cache=FALSE}
kable(dt_pl_n11$results$rsc)
```

### Wait List Size by Group
```{r echo=FALSE, cache=FALSE}
dt_sa_n11$res_plot$waitlist_plt %>% ggplotly()
```

### Reschedules by Group
```{r echo=FALSE, cache=FALSE}
dt_sa_n11$res_plot$rsc_plt %>% ggplotly()
```





NP, 1.2
=======================================================================

Row {data-height=650}
-----------------------------------------------------------------------

### Policy Description
The policy description is based on a graph to the right

* The left column shows MDP policy, the right column shows Myopic Policy
* The top row shows scheduling costs, adjusted for resource usage, the bottom is not adjusted
* The bottom row shows in which days the policy will allow scheduling.
* The top row shows approximate order of patient scheduling.

Additionally the far right graph shows the approximate decision making 

Policy: 

* MDP will schedule into the first 2 days only. 
* In this approximate order:

  * Surgery 6, Complexity 1
  * Surgery 6, Complexity 2
  * Surgery 1, Complexity 1
  * Surgery 4, Complexity 2
  * Surgery 4, Complexity 1 / Surgery 1, Complexity 2

### Policy Math Graph

```{r echo=FALSE}
dt_zs_n12$zf_plt %>% ggplotly()
```

### Policy Evidence Graph

```{r echo=FALSE}
dt_sa_n12$res_plot$sched_plt %>% ggplotly()
```

Row 
-----------------------------------------------------------------------

### Wait Times in weeks
```{r echo=FALSE, cache=FALSE}
kable(dt_pl_n12$results$pw)
```

### Wait List Size
```{r echo=FALSE, cache=FALSE}
kable(dt_pl_n12$results$wtl)
```

### Transitions per week
```{r echo=FALSE, cache=FALSE}
kable(dt_pl_n12$results$tr)
```

### Utilization
```{r echo=FALSE, cache=FALSE}
kable(dt_pl_n12$results$util)
```

Row
-----------------------------------------------------------------------

### Reschedules
```{r echo=FALSE, cache=FALSE}
kable(dt_pl_n12$results$rsc)
```

### Wait List Size by Group
```{r echo=FALSE, cache=FALSE}
dt_sa_n12$res_plot$waitlist_plt %>% ggplotly()
```

### Reschedules by Group
```{r echo=FALSE, cache=FALSE}
dt_sa_n12$res_plot$rsc_plt %>% ggplotly()
```





NP, 1.3
=======================================================================

Row {data-height=650}
-----------------------------------------------------------------------

### Policy Description
The policy description is based on a graph to the right

* The left column shows MDP policy, the right column shows Myopic Policy
* The top row shows scheduling costs, adjusted for resource usage, the bottom is not adjusted
* The bottom row shows in which days the policy will allow scheduling.
* The top row shows approximate order of patient scheduling.

Additionally the far right graph shows the approximate decision making 

Policy: 

* MDP will schedule into the first 2 days only. 
* In this approximate order:

  * Surgery 6, Complexity 1
  * Surgery 6, Complexity 2
  * Surgery 1, Complexity 1
  * Surgery 4, Complexity 2
  * Surgery 4, Complexity 1 / Surgery 1, Complexity 2

### Policy Math Graph

```{r echo=FALSE}
dt_zs_n13$zf_plt %>% ggplotly()
```

### Policy Evidence Graph

```{r echo=FALSE}
dt_sa_n13$res_plot$sched_plt %>% ggplotly()
```

Row 
-----------------------------------------------------------------------

### Wait Times in weeks
```{r echo=FALSE, cache=FALSE}
kable(dt_pl_n13$results$pw)
```

### Wait List Size
```{r echo=FALSE, cache=FALSE}
kable(dt_pl_n13$results$wtl)
```

### Transitions per week
```{r echo=FALSE, cache=FALSE}
kable(dt_pl_n13$results$tr)
```

### Utilization
```{r echo=FALSE, cache=FALSE}
kable(dt_pl_n13$results$util)
```

Row
-----------------------------------------------------------------------

### Reschedules
```{r echo=FALSE, cache=FALSE}
kable(dt_pl_n13$results$rsc)
```

### Wait List Size by Group
```{r echo=FALSE, cache=FALSE}
dt_sa_n13$res_plot$waitlist_plt %>% ggplotly()
```

### Reschedules by Group
```{r echo=FALSE, cache=FALSE}
dt_sa_n13$res_plot$rsc_plt %>% ggplotly()
```





P, 1
=======================================================================

Row {data-height=650}
-----------------------------------------------------------------------

### Policy Description
The policy description is based on a graph to the right

* The left column shows MDP policy, the right column shows Myopic Policy
* The top row shows scheduling costs, adjusted for resource usage, the bottom is not adjusted
* The bottom row shows in which days the policy will allow scheduling.
* The top row shows approximate order of patient scheduling.

Additionally the far right graph shows the approximate decision making 

Policy: 

* MDP will schedule S6 and S1C1 into the first 2 days only. S4 and S1C2 will be scheduled into the entire planning horizon.
* In this approximate order:

  * Surgery 4, Complexity 1 / Surgery 1, Complexity 2
  * Surgery 4, Complexity 2
  * Surgery 6, Complexity 1
  * Surgery 6, Complexity 2
  * Surgery 1, Complexity 1


### Policy Math Graph

```{r echo=FALSE}
dt_zs_y10$zf_plt %>% ggplotly()
```

### Policy Evidence Graph

```{r echo=FALSE}
dt_sa_y10$res_plot$sched_plt %>% ggplotly()
```

Row 
-----------------------------------------------------------------------

### Wait Times in weeks
```{r echo=FALSE, cache=FALSE}
kable(dt_pl_y10$results$pw)
```

### Wait List Size
```{r echo=FALSE, cache=FALSE}
kable(dt_pl_y10$results$wtl)
```

### Transitions per week
```{r echo=FALSE, cache=FALSE}
kable(dt_pl_y10$results$tr)
```

### Utilization
```{r echo=FALSE, cache=FALSE}
kable(dt_pl_y10$results$util)
```

Row
-----------------------------------------------------------------------

### Reschedules
```{r echo=FALSE, cache=FALSE}
kable(dt_pl_y10$results$rsc)
```

### Wait List Size by Group
```{r echo=FALSE, cache=FALSE}
dt_sa_y10$res_plot$waitlist_plt %>% ggplotly()
```

### Reschedules by Group
```{r echo=FALSE, cache=FALSE}
dt_sa_y10$res_plot$rsc_plt %>% ggplotly()
```





P, 1.1
=======================================================================

Row {data-height=650}
-----------------------------------------------------------------------

### Policy Description
The policy description is based on a graph to the right

* The left column shows MDP policy, the right column shows Myopic Policy
* The top row shows scheduling costs, adjusted for resource usage, the bottom is not adjusted
* The bottom row shows in which days the policy will allow scheduling.
* The top row shows approximate order of patient scheduling.

Additionally the far right graph shows the approximate decision making 

Policy: 

* MDP will schedule S6 and S1C1 into the first 2 days only. S4 and S1C2 will be scheduled into the entire planning horizon.
* In this approximate order:

  * Surgery 4, Complexity 1 / Surgery 1, Complexity 2
  * Surgery 4, Complexity 2
  * Surgery 6, Complexity 1
  * Surgery 1, Complexity 1
  * Surgery 6, Complexity 2

### Policy Math Graph

```{r echo=FALSE}
dt_zs_y11$zf_plt %>% ggplotly()
```

### Policy Evidence Graph

```{r echo=FALSE}
dt_sa_y11$res_plot$sched_plt %>% ggplotly()
```

Row 
-----------------------------------------------------------------------

### Wait Times in weeks
```{r echo=FALSE, cache=FALSE}
kable(dt_pl_y11$results$pw)
```

### Wait List Size
```{r echo=FALSE, cache=FALSE}
kable(dt_pl_y11$results$wtl)
```

### Transitions per week
```{r echo=FALSE, cache=FALSE}
kable(dt_pl_y11$results$tr)
```

### Utilization
```{r echo=FALSE, cache=FALSE}
kable(dt_pl_y11$results$util)
```

Row
-----------------------------------------------------------------------

### Reschedules
```{r echo=FALSE, cache=FALSE}
kable(dt_pl_y11$results$rsc)
```

### Wait List Size by Group
```{r echo=FALSE, cache=FALSE}
dt_sa_y11$res_plot$waitlist_plt %>% ggplotly()
```

### Reschedules by Group
```{r echo=FALSE, cache=FALSE}
dt_sa_y11$res_plot$rsc_plt %>% ggplotly()
```





P, 1.2
=======================================================================

Row {data-height=650}
-----------------------------------------------------------------------

### Policy Description
The policy description is based on a graph to the right

* The left column shows MDP policy, the right column shows Myopic Policy
* The top row shows scheduling costs, adjusted for resource usage, the bottom is not adjusted
* The bottom row shows in which days the policy will allow scheduling.
* The top row shows approximate order of patient scheduling.

Additionally the far right graph shows the approximate decision making 

Policy: 

* MDP will schedule S6 and S1C1 into the first 2 days only. S4 and S1C2 will be scheduled into the entire planning horizon.
* In this approximate order:

  * Surgery 4, Complexity 1 / Surgery 1, Complexity 2
  * Surgery 4, Complexity 2
  * Surgery 6, Complexity 1
  * Surgery 1, Complexity 1
  * Surgery 6, Complexity 2

### Policy Math Graph

```{r echo=FALSE}
dt_zs_y12$zf_plt %>% ggplotly()
```

### Policy Evidence Graph

```{r echo=FALSE}
dt_sa_y12$res_plot$sched_plt %>% ggplotly()
```

Row 
-----------------------------------------------------------------------

### Wait Times in weeks
```{r echo=FALSE, cache=FALSE}
kable(dt_pl_y12$results$pw)
```

### Wait List Size
```{r echo=FALSE, cache=FALSE}
kable(dt_pl_y12$results$wtl)
```

### Transitions per week
```{r echo=FALSE, cache=FALSE}
kable(dt_pl_y12$results$tr)
```

### Utilization
```{r echo=FALSE, cache=FALSE}
kable(dt_pl_y12$results$util)
```

Row
-----------------------------------------------------------------------

### Reschedules
```{r echo=FALSE, cache=FALSE}
kable(dt_pl_y12$results$rsc)
```

### Wait List Size by Group
```{r echo=FALSE, cache=FALSE}
dt_sa_y12$res_plot$waitlist_plt %>% ggplotly()
```

### Reschedules by Group
```{r echo=FALSE, cache=FALSE}
dt_sa_y12$res_plot$rsc_plt %>% ggplotly()
```





P, 1.3
=======================================================================

Row {data-height=650}
-----------------------------------------------------------------------

### Policy Description
The policy description is based on a graph to the right

* The left column shows MDP policy, the right column shows Myopic Policy
* The top row shows scheduling costs, adjusted for resource usage, the bottom is not adjusted
* The bottom row shows in which days the policy will allow scheduling.
* The top row shows approximate order of patient scheduling.

Additionally the far right graph shows the approximate decision making 

Policy: 

* MDP will schedule S6 and S1C1 into the first 2 days only. S4 and S1C2 will be scheduled into the entire planning horizon.
* In this approximate order:

  * Surgery 4, Complexity 1 / Surgery 1, Complexity 2
  * Surgery 4, Complexity 2
  * Surgery 6, Complexity 1
  * Surgery 1, Complexity 1
  * Surgery 6, Complexity 2

### Policy Math Graph

```{r echo=FALSE}
dt_zs_y13$zf_plt %>% ggplotly()
```

### Policy Evidence Graph

```{r echo=FALSE}
dt_sa_y13$res_plot$sched_plt %>% ggplotly()
```

Row 
-----------------------------------------------------------------------

### Wait Times in weeks
```{r echo=FALSE, cache=FALSE}
kable(dt_pl_y13$results$pw)
```

### Wait List Size
```{r echo=FALSE, cache=FALSE}
kable(dt_pl_y13$results$wtl)
```

### Transitions per week
```{r echo=FALSE, cache=FALSE}
kable(dt_pl_y13$results$tr)
```

### Utilization
```{r echo=FALSE, cache=FALSE}
kable(dt_pl_y13$results$util)
```

Row
-----------------------------------------------------------------------

### Reschedules
```{r echo=FALSE, cache=FALSE}
kable(dt_pl_y13$results$rsc)
```

### Wait List Size by Group
```{r echo=FALSE, cache=FALSE}
dt_sa_y13$res_plot$waitlist_plt %>% ggplotly()
```

### Reschedules by Group
```{r echo=FALSE, cache=FALSE}
dt_sa_y13$res_plot$rsc_plt %>% ggplotly()
```